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Multilevel Modelling of Hierarchical Data in Developmental Studies

  • Michael H. Boyle (a1) and J. Douglas Willms (a2)

This report attempts to give nontechnical readers some insight into how a multilevel modelling framework can be used in longitudinal studies to assess contextual influences on child development when study samples arise from naturally formed groupings. We hope to achieve this objective by: (1) discussing the types of variables and research designs used for collecting developmental data; (2) presenting the methods and data requirements associated with two statistical approaches to developmental data—growth curve modelling and discrete-time survival analysis; (3) describing the multilevel extensions of these approaches, which can be used when the study of development includes intact clusters or naturally formed groupings; (4) demonstrating the flexibility of these two approaches for addressing a variety of research questions; and (5) placing the multilevel framework developed in this report in the context of some important issues, alternative approaches, and recent developments. We hope that readers new to these methods are able to visualize the possibility of using them to advance their work.

Corresponding author
Requests for reprints to: Michael H. Boyle, Centre for Studies for Children at Risk, McMaster University Faculty of Health Sciences and Hamilton Health Sciences Corporation, Patterson Building, Chedoke Division, Hamilton Health Sciences Corporation, Box 2000, Hamilton, Ontario, Canada L8N 3Z5 (E-mail:
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The Journal of Child Psychology and Psychiatry and Allied Disciplines
  • ISSN: 0021-9630
  • EISSN: 1469-7610
  • URL: /core/journals/journal-of-child-psychology-and-psychiatry-and-allied-disciplines
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